/// <summary>Computations that depend on the observed value of numberOfIterationsDecreased and WeightPriors and FeatureIndexes and InstanceCount and InstanceFeatureCounts and FeatureValues and Labels and WeightConstraints</summary>
/// <param name="numberOfIterations">The number of times to iterate each loop</param>
private void Changed_numberOfIterationsDecreased_WeightPriors_FeatureIndexes_InstanceCount_InstanceFeatureCounts_7(int numberOfIterations)
{
if (this.Changed_numberOfIterationsDecreased_WeightPriors_FeatureIndexes_InstanceCount_InstanceFeatureCounts_7_iterationsDone == numberOfIterations)
{
return;
}
for (int iteration = this.Changed_numberOfIterationsDecreased_WeightPriors_FeatureIndexes_InstanceCount_InstanceFeatureCounts_7_iterationsDone; iteration < numberOfIterations; iteration++)
{
// Message to 'Weights_uses' from Replicate factor
this.Weights_uses_B_toDef = ReplicateOp_Divide.ToDef <DistributionStructArray <Gaussian, double> >(this.Weights_uses_B, this.Weights_uses_B_toDef);
// Message to 'Weights_uses' from Replicate factor
this.Weights_uses_F_marginal = ReplicateOp_Divide.Marginal <DistributionStructArray <Gaussian, double> >(this.Weights_uses_B_toDef, this.weightPriors, this.Weights_uses_F_marginal);
// Message to 'Weights_uses' from Replicate factor
this.Weights_uses_F[1] = ReplicateOp_Divide.UsesAverageConditional <DistributionStructArray <Gaussian, double> >(this.Weights_uses_B[1], this.Weights_uses_F_marginal, 1, this.Weights_uses_F[1]);
// Message to 'Weights_FeatureIndexes' from JaggedSubarray factor
this.Weights_uses_F_1__marginal = JaggedSubarrayOp <double> .Marginal <DistributionStructArray <Gaussian, double>, Gaussian, object, DistributionStructArray <Gaussian, double> >(this.Weights_uses_F[1], this.IndexedWeights_B, this.featureIndexes, this.Weights_uses_F_1__marginal);
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
// Message to 'Weights_FeatureIndexes' from JaggedSubarray factor
this.Weights_FeatureIndexes_F[InstanceRange] = JaggedSubarrayOp <double> .ItemsAverageConditional <DistributionStructArray <Gaussian, double>, Gaussian, DistributionStructArray <Gaussian, double> >(this.IndexedWeights_B[InstanceRange], this.Weights_uses_F[1], this.Weights_uses_F_1__marginal, this.featureIndexes, InstanceRange, this.Weights_FeatureIndexes_F[InstanceRange]);
for (int InstanceFeatureRanges = 0; InstanceFeatureRanges < this.instanceFeatureCounts[InstanceRange]; InstanceFeatureRanges++)
{
// Message to 'FeatureScores' from Product factor
this.FeatureScores_F[InstanceRange][InstanceFeatureRanges] = GaussianProductOp.ProductAverageConditional(this.featureValues[InstanceRange][InstanceFeatureRanges], this.Weights_FeatureIndexes_F[InstanceRange][InstanceFeatureRanges]);
}
// Message to 'Score' from Sum factor
this.Score_F[InstanceRange] = FastSumOp.SumAverageConditional(this.FeatureScores_F[InstanceRange]);
// Message to 'NoisyScore' from GaussianFromMeanAndVariance factor
this.NoisyScore_F[InstanceRange] = GaussianFromMeanAndVarianceOp.SampleAverageConditional(this.Score_F[InstanceRange], 1.0);
// Message to 'NoisyScore_use' from IsPositive factor
this.NoisyScore_use_B[InstanceRange] = IsPositiveOp_Proper.XAverageConditional(Bernoulli.PointMass(this.labels[InstanceRange]), this.NoisyScore_F[InstanceRange]);
// Message to 'Score' from GaussianFromMeanAndVariance factor
this.Score_B[InstanceRange] = GaussianFromMeanAndVarianceOp.MeanAverageConditional(this.NoisyScore_use_B[InstanceRange], 1.0);
// Message to 'FeatureScores' from Sum factor
this.FeatureScores_B[InstanceRange] = FastSumOp.ArrayAverageConditional <DistributionStructArray <Gaussian, double> >(this.Score_B[InstanceRange], this.Score_F[InstanceRange], this.FeatureScores_F[InstanceRange], this.FeatureScores_B[InstanceRange]);
for (int InstanceFeatureRanges = 0; InstanceFeatureRanges < this.instanceFeatureCounts[InstanceRange]; InstanceFeatureRanges++)
{
// Message to 'IndexedWeights' from Product factor
this.IndexedWeights_B[InstanceRange][InstanceFeatureRanges] = GaussianProductOp.BAverageConditional(this.FeatureScores_B[InstanceRange][InstanceFeatureRanges], this.featureValues[InstanceRange][InstanceFeatureRanges]);
}
this.Weights_uses_F_1__marginal = JaggedSubarrayOp <double> .MarginalIncrement <DistributionStructArray <Gaussian, double>, Gaussian, DistributionStructArray <Gaussian, double> >(this.Weights_uses_F_1__marginal, this.Weights_FeatureIndexes_F[InstanceRange], this.IndexedWeights_B[InstanceRange], this.featureIndexes, InstanceRange);
}
// Message to 'Weights_uses' from JaggedSubarray factor
this.Weights_uses_B[1] = JaggedSubarrayOp <double> .ArrayAverageConditional <Gaussian, DistributionStructArray <Gaussian, double>, DistributionStructArray <Gaussian, double> >(this.IndexedWeights_B, this.featureIndexes, this.Weights_uses_B[1]);
this.OnProgressChanged(new ProgressChangedEventArgs(iteration));
}
// Message to 'Weights_uses' from Replicate factor
this.Weights_uses_B_toDef = ReplicateOp_Divide.ToDef <DistributionStructArray <Gaussian, double> >(this.Weights_uses_B, this.Weights_uses_B_toDef);
// Message to 'Weights_marginal' from Variable factor
this.Weights_marginal_F = VariableOp.MarginalAverageConditional <DistributionStructArray <Gaussian, double> >(this.Weights_uses_B_toDef, this.weightPriors, this.Weights_marginal_F);
this.Changed_numberOfIterationsDecreased_WeightPriors_FeatureIndexes_InstanceCount_InstanceFeatureCounts_7_iterationsDone = numberOfIterations;
}
/// <summary>Computations that depend on the observed value of FeatureIndexes and WeightPriors and WeightConstraints and InstanceCount and InstanceFeatureCounts</summary>
private void Changed_FeatureIndexes_WeightPriors_WeightConstraints_InstanceCount_InstanceFeatureCounts()
{
if (this.Changed_FeatureIndexes_WeightPriors_WeightConstraints_InstanceCount_InstanceFeatureCounts_iterationsDone == 1)
{
return;
}
// Message to 'Weights_FeatureIndexes' from JaggedSubarray factor
this.Weights_uses_F_1__marginal = JaggedSubarrayOp <double> .Marginal <DistributionStructArray <Gaussian, double>, Gaussian, object, DistributionStructArray <Gaussian, double> >(this.Weights_uses_F[1], this.IndexedWeights_B, this.featureIndexes, this.Weights_uses_F_1__marginal);
for (int InstanceRange = 0; InstanceRange < this.instanceCount; InstanceRange++)
{
// Message to 'Weights_FeatureIndexes' from JaggedSubarray factor
this.Weights_FeatureIndexes_F[InstanceRange] = JaggedSubarrayOp <double> .ItemsAverageConditional <DistributionStructArray <Gaussian, double>, Gaussian, DistributionStructArray <Gaussian, double> >(this.IndexedWeights_B[InstanceRange], this.Weights_uses_F[1], this.Weights_uses_F_1__marginal, this.featureIndexes, InstanceRange, this.Weights_FeatureIndexes_F[InstanceRange]);
}
this.Changed_FeatureIndexes_WeightPriors_WeightConstraints_InstanceCount_InstanceFeatureCounts_iterationsDone = 1;
this.Changed_InstanceCount_InstanceFeatureCounts_FeatureValues_FeatureIndexes_WeightPriors_WeightConstrai6_iterationsDone = 0;
}
/// <summary>Computations that depend on the observed value of WeightPriors and must reset on changes to numberOfIterationsDecreased and FeatureIndexes and InstanceCount and InstanceFeatureCounts and FeatureValues and Labels and WeightConstraints</summary>
/// <param name="initialise">If true, reset messages that initialise loops</param>
private void Changed_WeightPriors_Init_numberOfIterationsDecreased_FeatureIndexes_InstanceCount_InstanceFeatureCo1(bool initialise)
{
if ((this.Changed_WeightPriors_Init_numberOfIterationsDecreased_FeatureIndexes_InstanceCount_InstanceFeatureCo1_iterationsDone == 1) && ((!initialise) || this.Changed_WeightPriors_Init_numberOfIterationsDecreased_FeatureIndexes_InstanceCount_InstanceFeatureCo1_isInitialised))
{
return;
}
for (int _ind = 0; _ind < 2; _ind++)
{
this.Weights_uses_B[_ind] = ArrayHelper.MakeUniform <DistributionStructArray <Gaussian, double> >(this.weightPriors);
}
// Message to 'Weights_FeatureIndexes' from JaggedSubarray factor
this.Weights_uses_F_1__marginal = JaggedSubarrayOp <double> .MarginalInit <DistributionStructArray <Gaussian, double> >(this.Weights_uses_F[1]);
this.Changed_WeightPriors_Init_numberOfIterationsDecreased_FeatureIndexes_InstanceCount_InstanceFeatureCo1_iterationsDone = 1;
this.Changed_WeightPriors_Init_numberOfIterationsDecreased_FeatureIndexes_InstanceCount_InstanceFeatureCo1_isInitialised = true;
this.Changed_WeightConstraints_WeightPriors_Init_numberOfIterationsDecreased_FeatureIndexes_InstanceCount3_iterationsDone = 0;
this.Changed_numberOfIterationsDecreased_WeightPriors_FeatureIndexes_InstanceCount_InstanceFeatureCounts_7_iterationsDone = 0;
}
/// <summary>Computations that depend on the observed value of WeightPriors</summary>
private void Changed_WeightPriors()
{
if (this.Changed_WeightPriors_iterationsDone == 1)
{
return;
}
for (int _ind = 0; _ind < 2; _ind++)
{
this.Weights_uses_B[_ind] = ArrayHelper.MakeUniform <DistributionStructArray <Gaussian, double> >(this.weightPriors);
this.Weights_uses_F[_ind] = ArrayHelper.MakeUniform <DistributionStructArray <Gaussian, double> >(this.weightPriors);
}
// Message to 'Weights_uses' from Replicate factor
this.Weights_uses_F_marginal = ReplicateOp_Divide.MarginalInit <DistributionStructArray <Gaussian, double> >(this.weightPriors);
// Message to 'Weights_uses' from Replicate factor
this.Weights_uses_B_toDef = ReplicateOp_Divide.ToDefInit <DistributionStructArray <Gaussian, double> >(this.weightPriors);
// Message to 'Weights_FeatureIndexes' from JaggedSubarray factor
this.Weights_uses_F_1__marginal = JaggedSubarrayOp <double> .MarginalInit <DistributionStructArray <Gaussian, double> >(this.Weights_uses_F[1]);
this.Changed_WeightPriors_iterationsDone = 1;
this.Changed_WeightConstraints_WeightPriors_iterationsDone = 0;
this.Changed_FeatureIndexes_WeightPriors_WeightConstraints_InstanceCount_InstanceFeatureCounts_iterationsDone = 0;
}